---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- medical-imaging
- cephalometric
- landmark-detection
- orthodontics
- heatmap-regression
- spatial-priors
- onnx
library_name: onnxruntime
pipeline_tag: image-segmentation
datasets:
- custom
metrics:
- mre
- sdr
model-index:
- name: CephTrace v4
  results:
  - task:
      type: landmark-detection
      name: Cephalometric Landmark Detection
    dataset:
      type: custom
      name: Aggregated (ISBI 2015 + Aariz/CEPHA29 + DentalCepha)
      config: 25-landmark
      split: test
    metrics:
    - type: mean-radial-error
      value: 1.050
      name: MRE (mm)
    - type: sdr-2mm
      value: 87.8
      name: SDR@2mm (%)
---

# CephTrace v4 — Anatomy-Guided Cephalometric Landmark Detection

**1.050 mm MRE across 25 landmarks** on a 151-image held-out test set, using image-adaptive spatial priors generated by anatomical analysis of each radiograph.

## Model Description

CephTrace v4 is a two-stage pipeline for automatic cephalometric landmark detection from lateral skull radiographs:

- **Stage 0 (Anatomical Initialization):** A multi-phase module that detects the soft-tissue profile, partitions the image into anatomical zones, extracts bony contours, derives anchor landmarks via geometric rules, and generates 25 per-landmark Gaussian attention maps — all adapted to each patient's individual anatomy.
- **Stage 1 (Heatmap Regression):** An HRNet-W32 backbone (32M params) that accepts the 28-channel input (3 RGB + 25 attention maps) and outputs 25 landmark heatmaps at 256×256 resolution.

The key innovation is that the attention priors are **image-adaptive**: each patient receives maps centered at *their* estimated anatomy, not fixed population-average positions. Controlled experiments show this reduces MRE by 30.9% compared to the same architecture without priors.

## ONNX Models

All models are exported as ONNX (opset 14) for cross-platform inference.

| File | Stage | Purpose | Size | Input | Output |
|------|-------|---------|------|-------|--------|
| `v4_stage0_profile.onnx` | 0A | Soft-tissue profile segmentation | 26.8 MB | `(1,1,512,512)` float32 | `(1,1,512,512)` sigmoid mask |
| `z1_cranial_base_contours.onnx` | 0C | Cranial base contour segmentation | 26.8 MB | `(1,1,256,256)` float32 | `(1,1,256,256)` logits |
| `z2_midface_contours.onnx` | 0C | Midface contour segmentation (palatal + upper incisor) | 26.8 MB | `(1,1,256,256)` float32 | `(1,2,256,256)` logits |
| `z3_mandible_contours.onnx` | 0C | Mandible contour segmentation (border + symphysis + lower incisor) | 26.8 MB | `(1,1,256,256)` float32 | `(1,3,256,256)` logits |
| `z4_posterior_contours.onnx` | 0C | Posterior contour segmentation (mandible + cranial base) | 26.8 MB | `(1,1,256,256)` float32 | `(1,2,256,256)` logits |
| `phase0e_model.onnx` | 0E | Anchor → derived landmark MLP | 455 KB | `(1,14)` float32 | `(1,36)` float32 |
| `v4_stage1.onnx` | 1 | HRNet-W32 heatmap regression | 130 MB | `(1,28,512,512)` float32 | `(1,25,256,256)` float32 |

**Total: 264 MB**

## Pipeline Flow

```
Lateral Cephalogram (any resolution)
    │
    ▼ resize to 512×512
Phase 0A ──► Soft-tissue profile mask (Dice 0.80)
    │
    ▼
Phase 0B ──► 5 anatomical zones + 6 soft-tissue landmarks (geometric rules)
    │
    ▼ per-zone CLAHE enhancement
Phase 0C ──► Bony contour masks (4 zone-specific U-Nets)
    │
    ▼ Douglas-Peucker simplification
Phase 0D ──► 7 anchor landmarks (0.11 mm MRE, topological rules)
    │
    ▼
Phase 0E ──► 18 derived landmarks (MLP, 114K params)
             + 25 Gaussian attention maps (256×256, 3-tier σ)
    │
    ▼ bilinear upsample to 512, concat with RGB → 28 channels
Stage 1  ──► 25 heatmaps (256×256) → peak decode → 25 landmarks
```

**Inference time:** ~410 ms total (Stage 0: ~40 ms, Stage 1: ~350 ms) on A100 GPU.

## Landmark Set (25 landmarks, CANONICAL_25 order)

```
 0: S (Sella)           1: N (Nasion)          2: Or (Orbitale)
 3: Po (Porion)         4: ANS                 5: PNS
 6: A (Subspinale)      7: B (Supramentale)    8: Pog (Pogonion)
 9: Gn (Gnathion)      10: Me (Menton)        11: Go (Gonion)
12: Ar (Articulare)    13: Co (Condylion)     14: U1_tip
15: U1_root            16: L1_tip             17: L1_root
18: UL (Upper Lip)     19: LL (Lower Lip)     20: Pm (Pterygomaxillare)
21: Ba (Basion)        22: Pog_soft           23: Sn (Subnasale)
24: Prn (Pronasale)
```

## Performance

### Controlled Ablation (151-image held-out test set)

| Configuration | Input | MRE (mm) | SDR@2mm |
|---|---|---|---|
| HRNet backbone (no priors) | 3-ch | 1.520 | 86.6% |
| **HRNet + Phase 0E priors** | **28-ch** | **1.050** | **87.8%** |
| **Improvement** | | **0.470 (30.9%)** | **+1.2%** |

Same 1,201 training images, architecture, and recipe. Only variable: prior channels.

### Prior Ablation

| Configuration | MRE (mm) | vs. No Priors |
|---|---|---|
| Random priors (shuffled channels) | 2.240 | +15.6% worse |
| No priors (baseline) | 1.938 | — |
| Fixed textbook priors | 1.869 | −3.6% (marginal) |
| **Image-adaptive priors (Phase 0E)** | **1.043** | **−46.2%** |

### Attention Map Confidence Tiers

| Tier | σ (at 256×256) | Landmarks | Mean Improvement |
|---|---|---|---|
| High | 5–7 | S, N, Me, ANS, Prn, Sn | −0.74 mm |
| Medium | 8–13 | Go, Gn, Pog, Or, UL, LL, Pog', A | −0.44 mm |
| Low | 18–22 | Po, Co, B, PNS, U1r, L1r, Ba, Pm | −0.17 mm |

### Clinical Reliability

- Vertical skeletal classification (FMA): Cohen's κ = 0.78 (substantial agreement)
- 20/25 landmarks improve with priors; 1 degrades (Basion, lowest confidence tier)

## Usage

```python
import onnxruntime as ort
import numpy as np
import cv2

# Load Stage 1 model
sess = ort.InferenceSession("v4_stage1.onnx")

# Prepare input (28 channels: 3 RGB + 25 attention maps from Stage 0)
image = cv2.imread("cephalogram.jpg")
image_512 = cv2.resize(image, (512, 512))
rgb = image_512.astype(np.float32) / 255.0  # (512, 512, 3)
rgb = np.transpose(rgb, (2, 0, 1))  # (3, 512, 512)

# attention_maps shape: (25, 512, 512) from Stage 0 pipeline
# (See Stage 0 inference code for generating these)
input_28ch = np.concatenate([rgb, attention_maps], axis=0)  # (28, 512, 512)
input_tensor = input_28ch[np.newaxis]  # (1, 28, 512, 512)

# Run inference
input_name = sess.get_inputs()[0].name
heatmaps = sess.run(None, {input_name: input_tensor})[0]  # (1, 25, 256, 256)

# Decode landmarks from heatmap peaks
landmarks = []
for i in range(25):
    hm = heatmaps[0, i]
    y, x = np.unravel_index(np.argmax(hm), hm.shape)
    # Scale from heatmap (256) to image (512) coordinates
    landmarks.append((x * 2, y * 2))
```

## Training Data

Aggregated from three public sources (1,502 total images):

| Source | Images | Landmarks | Scanner(s) |
|---|---|---|---|
| [ISBI 2015](https://www-o.ntust.edu.tw/~cweiwang/ISBI2015/challenge1/) | 400 | 19 | Soredex CRANEX |
| [Aariz/CEPHA29](https://doi.org/10.1038/s41597-025-05542-3) | 1,000 | 29 | 7+ device types |
| DentalCepha | 102 | 19 | Mixed |

Split: 1,201 train / 150 validation / 151 test (stratified by source, seed=42).

## Citation

```bibtex
@article{mohapatra2025cephtrace,
  title={CephTrace: Anatomy-Guided Spatial Attention Priors for
         Sub-Millimeter Cephalometric Landmark Detection},
  author={Mohapatra, Sidhartha and Mohanty, Pallavi},
  journal={arXiv preprint arXiv:2605.03358},
  year={2025},
  url={https://arxiv.org/abs/2605.03358}
}
```

## Links

| Resource | URL |
|---|---|
| **Paper** | [arXiv:2605.03358](https://arxiv.org/abs/2605.03358) |
| **Code** | [github.com/sidwiz/cephtrace-research](https://github.com/sidwiz/cephtrace-research) |
| **Data & Weights** | [Zenodo DOI 10.5281/zenodo.20032162](https://doi.org/10.5281/zenodo.20032162) |
| **Website** | [cephtrace.com](https://cephtrace.com) |

## License

This work is licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). Commercial use requires a separate license — contact research@cephtrace.com.

Three U.S. provisional patent applications are pending (#64/037,246; #64/037,252; #64/039,042).

## Limitations

- Trained on 2D lateral cephalograms only; not validated on 3D CBCT or PA cephalograms.
- Phase 0A requires visible soft-tissue profile; severely overexposed or cropped images may degrade.
- Basion (Ba) accuracy degrades slightly with priors due to low Phase 0E confidence (σ=22).
- Cross-source generalization without priors is poor (22–37 mm MRE in LOSO experiments); Phase 0's anatomical analysis provides scanner-invariant features.